code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_required(labels)
      c_f.ref_not_supported(embeddings, labels, ref_emb, ref_labels)
      (
          anchor_idx,
          positive_idx,
      ) = lmu.convert_to_pos_pairs_with_unique_labels(
          indices_tuple, labels
      )
      self.num_pairs = len(anchor_idx)
      if self.num_pairs == 0:
          return self.zero_losses()
      anchors, positives = (
          embeddings[anchor_idx],
          embeddings[positive_idx],
      )
      targets = c_f.to_device(torch.arange(self.num_pairs), embeddings)
      sim_mat = self.distance(anchors, positives)
      if not self.distance.is_inverted:
          sim_mat = -sim_mat
      return {
          "loss": {
              "losses": self.cross_entropy(sim_mat, targets),
              "indices": anchor_idx,
              "reduction_type": "element",
          }
      }
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/n_pairs_loss.py
imports: import torch
  from ..distances import DotProductSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 2 for calling the NPairsLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: dict, self